Doc. Ing. Ivo Bukovský, Ph.D.

Associate Professor, Division Head (U12110.3)
U 12110 Department of Instrumentation and Control Engineering
Division of Automatic Control and Engineering Informatics
Faculty of Mechanical Engineering
Czech Technical University in Prague
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IEEE CIS Neural Networks Technical Committee

IEEE CIS SAS

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E-mail: Ivo.Bukovsky@fs.cvut.cz

http://users.fs.cvut.cz/ivo.bukovsky/ (home web)
http://control.fs.cvut.cz/cz/lide/bukovsky-ivo (department web)
http://www.fs.cvut.cz/~bukovsky
Phone: +420 22435 2529, (from US and Canada: dial 011 + 420 + 2 2435 2529)

Research Interests:

·          Adaptive novelty detection: Learning Entropy

·          Adaptive algorithms and neural networks for complicated dynamic systems and signals

·          Multi-scale analysis approaches to signal processing and dynamic systems

·          Time series analysis and prediction

·          Nonlinear adaptive control

·           Fuzzy-logic rule based systems for modeling and evaluation of complex dynamic systems:

-                                  Applications of Type-2 Fuzzy Sets.

New

[1]         Bukovsky, I., Homma, N., et al: "A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications", BioMed Research International, issue Radiation Oncology and Medical Physics (ROMP) (open Access) DOI: 10.1155/2738, September, 2014. (10/09/2014 article in press - to appear)

Habilitation Thesis

[2]        Bukovsky, I.:  Nonconventional Neural Architectures and their Advantages for Technical Applications, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2012, ISBN: 978-80-01-05122-1

Ph.D. Thesis           

[3]        Bukovsky, I. : Modeling of Complex Dynamic Systems by Nonconventional Artificial Neural Architectures and Adaptive Approach to Evaluation of Chaotic Time Series, Ph.D. THESIS, Faculty of Mechanical Engineering, Czech Technical University in Prague (defended September 7, 2007, Siemens Excellence Award 2007, thesis  SUMMARY ).

Tutorial

[4]       Ivo Bukovsky, Jiri Bila, Madan M. Gupta, and Zeng-Guang Hou: NEW NEURAL ARCHITECTURES AND NEW ADAPTIVE EVALUATION OF CHAOTIC TIME SERIES, TUTORIAL for 2008 IEEE International Conference on AUTOMATION AND LOGISTICS, August 31 2008, 2:00pm – 5:00pm, Qingdao, China, 2008. organizers: Ivo Bukovsky (Czech Technical University in Prague, Czech Republic), Jiri Bila (Czech Technical University in Prague, Czech Republic), Madan M. Gupta (University of Saskatchewan, Canada), and Zeng-Guang Hou (The Chinese Academy of Sciences, China).

(Download pdf from IEEE CIS Multimedia Tutorials Center)

Book Chapters

[5]      Bukovsky, I., Bila. J: “Adaptive Evaluation of Complex Dynamic Systems using Low-Dimensional Neural Architectures”, in Advances in Cognitive Informatics and Cognitive Computing, Series: Studies in Computational Intelligence, Vol. 323/2010, eds. D. Zhang, Y. Wang, W. Kinsner, Springer-Verlag Berlin Heidelberg, 2010, ISBN: 978-3-642-16082-0, pp.33-57.

[6]      Gupta, M., M., Bukovsky, I., Homma, N., Solo M. G. A., Hou Z.-G.: “Fundamentals of Higher Order Neural Networks for Modeling and Simulation“, in Artificial Higher Order Neural Networks for Modeling and Simulation, ed. M. Zhang, IGI Global, 2012.

[7]      Rodriguez , R., Bukovsky, I., Homma, N.: “Potentials of Quadratic Neural Unit for Applications”, in Advances in Abstract Intelligence and Soft Computing, ed. Yingxu Wang, IGI Global, 2012.

[8]      Bukovsky, I., Bila, J., Gupta, M., M, Hou, Z-G., Homma, N.,.: “Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction” in Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence within the series of the Advances in Cognitive Informatics and Natural Intelligence (ACINI), ed. Y. Wang,  IGI Publishing, Hershey PA, USA,  2010. ISBN: 978-1-60566-902-1, pp.508-523.

[9]      Gupta, M., M, Homma, N., Hou, Z-G., Solo, M., G., Bukovsky, I.: “Higher Order Neural Networks: Fundamental Theory and Applications”, in Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications", ed. M. Zhang, IGI Global, 2010, ISBN 13: 978-1-61520-711-4, pp.397-422.

Journal Papers

[10]   A. Vagaská, P. Michal, I. Bukovský, M. Gombár and J. Kmec: "Mathematical Modelling and Description of the Technological Process of Aluminum Anodic Oxidation by Using the Neural Networks", International Journal of Materials, NAUN, Volume 1, 2014.

[11]   Bukovsky, I.: ¨Learning Entropy: Multiscale Measure for Incremental Learning¨, journal of Entropy, special issue on Dynamical Systems,, ISSN 1099–4300, 2013, 15(10), 4159-4187; doi:10.3390/e15104159

[12]   Bukovsky, I., Kolovratnik M.: „A Neural Network Model for Predicting NOx at the Mělník 1 Coal-powder Power Plant“, Acta Polytechnica ,Vol. 52 No. 3/2012,pp.17-22, ISSN 1805-2363, 2012.

[13]   Bila, J., Jura, J., Pokorny, J., Bukovsky, I.: “Qualitative Modeling and Monitoring of Selected Ecosystem Functions”, in Ecological Modelling, Volume 222, Issue 19, 10 October 2011, Elsevier, ISSN: 0304-3800, pp. 3640-3650.

[14]   Rodriguez , R., Bukovsky, I., Homma, N.: “Potentials of Quadratic Neural Unit for Applications”, in International Journal of Software Science and Computational Intelligence (IJSSCI) ,vol 3, issue 3, IGI Global, Publishing, Hershey PA, USA ISSN 1942-9045, DOI: 10.4018/jssci.2011070101 July-September 2011, pp.1-12.

[15]    Homma, N., Kato, S., Goto, T., Bukovsky, I., Kawashima, R., and Yoshizawa, M.: “How Can Brain Learn to Control a Nonholonomic System?”, in special issue of Journal of Robotics on   Volume 2010 (2010), Article ID 919306, 7 pages.

[16]    Bukovsky, I., Hou, Z-G., Bila, J., Gupta, M., M.: “Foundation of Nonconventional Neural Units and their Classification”, International Journal of Cognitive Informatics and Natural Intelligence (IJCiNi), 2(4), October-December 2008, IGI Publishing, Hershey PA, USA, ISSN 1557-3958, pp.29-43.

Local Journal Papers

[17]     Bukovsky, I., Rodriguez, R., Bila, J., Homma, N.: “Prospects of Gradient Methods for Nonlinear Control”,  Strojárstvo Extra, MEDIA/ST, s.r.o. publishing house, 2012, ISSN 1335-2938.

[18]    Bukovsky, I., Homma, N.: “Dynamic Backpropagation and Prediction (Dynamický backpropagation a predikce)” (in Czech), In: Automatizace, Vol. 53, No. 1-2, Prague, Czech Republic, Jan-Feb 2010, ISSN 0005-125X, pp.61-66.

[19]    Bukovsky,  I., Homma, N.: “Dynamic Backpropagation (Dynamický backpropagation)”  (in Czech),  In: Automatizace, Vol. 52, No. 10, Prague, Czech Republic, Oct 2009, ISSN 0005-125X, pp.586-590.

[20]    Bukovsky, I., Bila, J., Gupta, M., M.: “Linear Dynamic Neural Units with Time Delay for Identification and Control" (in Czech), In: Automatizace, Vol. 48, No. 10, Prague, Czech Republic, Oct 2005, ISSN 0005-125X, pp. 628-635.

[21]    Bíla, J., Vitkaj, J., Musil, M., Bukovsky, I.: “Some Limits of Neural Networks Use in Diagnostics” (in Czech),In.:  Automatizace, vol. 46, issue 11,  2003, Prague, ISSN 0005 -125X, pp.734-737.

Selected Conference Papers

[22]   Ivo Bukovsky, Cyril Oswald, Matous Cejnek, Peter M. Benes:" Learning Entropy for Novelty Detection A Cognitive Approach for Adaptive Filters", accepted paper for Sensor Signal Processing for Defence (SSPD) Conference 2014, Edinburgh, UK, Sept. 8-9, 2014

[23]    Ivo Bukovsky, Noriyasu Homma, Matous Cejnek and Kei Ichiji: "Study of Learning Entropy for Novelty Detection in Lung Tumor Motion Prediction for Target Tracking Radiation Therapy", The 2014 International Joint Conference on Neural Networks (IJCNN 2014), IEEE WCCI 2014, Beijing, 2014.

[24]    Peter Benes and Ivo Bukovsky: "Neural Network Approach to Hoist Deceleration Control", The 2014 International Joint Conference on Neural Networks (IJCNN 2014), IEEE WCCI 2014, Beijing, 2014.

[25]    Peter Michal, Jan Pitel, Alena Vagaska and Ivo Bukovsky: "Application of Neural Networks to Evaluate Experimental Data of Galvanic Zincing", The 2014 International Joint Conference on Neural Networks (IJCNN 2014), IEEE WCCI 2014, Beijing, 2014.

[26]    Bukovsky, I., Kinsner, W., Bila, J.: „Multiscale Analysis Approach for Novelty Detection in Adaptation Plot“, 3rd Sensor Signal Processing for Defence 2012 (SSPD 2012), Imperial College London, UK, September 24-27, 2012, doi: 10.1049/ic.2012.0114, E-ISBN: 978-1-84919-712-0.

[27]    Witold Kinsner, Simon Haykin, Yingxu Wang, Witold Pedrycz, Ivo Bukovsky, Bernard Widrow, Andrzej Skowron, Piotr Wasilewski, and Menahem Friedman: “Challenges in Engineering Education of Cognitive Dynamic Systems”, Proceedings of the Canadian Engineering Education Association 2012 (conf. CEEA12).

[28]    Bukovsky, I., Kolovratnik, M.: “Neural Network Model for Prediction of NOx at Coal-Powder Powerplant Mělník 1”, ERIN 2012, CTU in Prague, 25–27 April 2012, Czech Rep.

[29]    Bukovsky, I., Kinsner, W., Bila, J.: “Multiscale Approach to Uncertainty Evaluation of Input-Output DataAutomatizácia a riadenie v teórii a praxi ARTEP 2012, Slovakia 2012, ISBN: 978-80-553-0835-7.

[30]    Bukovsky, I., Kinsner, W., Maly, V., Krehlik, K.: “Multiscale Analysis of False Neighbors for State Space Reconstruction of Complicated Systems”, in proceedings of 2011 IEEE Symposium Series on Computational Inteligence (SSCI), IEEE Workshop CompSens 2011: ISBN 978-1-4577-0470-3, Paris 2011, pp. 35-41

[31]    Ichiji, K., Homma, N., Bukovsky, I., Yoshizawa, M..: “ Intelligent Sensing of Biomedical Signals - Lung Tumor Motion Prediction for Accurate Radiotherapy ”, in proceedings of 2011 IEEE Symposium Series on Computational inteligence (SSCI), IEEE Workshop CompSens 2011: ISBN 978-1-4577-0470-3, Paris 2011, pp. 65-72

[32]    Bukovsky, I., Lepold, M., Bila J.: Quadratic Neural Unit and its Network in Validation of Process Data of Steam Turbine Loop and Energetic Boiler”,WCCI 2010,  IEEE Int. Joint. Conf. on Neural Networks IJCNN, Barcelona, Spain, 2010.

[33]    Bukovsky, I., Ichiji, K., Homma, N., Yoshizawa, M.: Testing Potentials of Dynamic Quadratic Neural Unit for Prediction of Lung Motion during Respiration for Tracking Radiation Therapy”, WCCI 2010, IEEE Int. Joint. Conf. on Neural Networks IJCNN, Barcelona, Spain, 2010.

[34]    Bukovsky, I., Homma, N.,  Smetana, L., Rodriguez, R., Mironovova M., Vrana S.,: “Quadratic Neural Unit is a Good Compromise between Linear Models and Neural Networks for Industrial Applications, ICCI 2010 The 9th IEEE International Conference on Cognitive Informatics, Tsinghua University, Beijing, China, July 7-9, 2010.

[35]     Bukovsky, I., Anderle, F., Smetana, L.,:Quadratic Neural Unit for Adaptive Prediction of Transitions among Local Attractors of Lorenz System”, 2008 IEEE International Conference on AUTOMATION AND LOGISTICS, Qingdao, China, 2008, ISBN  978-1-4244-2503-7.

[36]    Bukovsky, I., Bila, J.: Adaptive Evaluation of Complex Time Series using Nonconventional Neural Units“, ICCI 2008,  The 7th IEEE International Conference on COGNITIVE INFORMATICS, California , USA, 2008, ISBN 9781424425389.

[37]    Simeunovic , G., Bukovsky, I.: The Implementation of the Dynamic-Order-Extended Time-Delay Dynamic Neural Units to Heat Transfer System Modelling”, 16th International Conference on NUCLEAR ENGINEERING (ICONE 16 ASME), Orlando, Florida, USA, May 11-15, 2008, ISBN 0-7918-3820-X.

[38]    Bukovsky, I., Hou, Z-G., Gupta, M., M., Bila, J.: “Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units”, ICCI 2007,  The 6th IEEE International Conference on COGNITIVE INFORMATICS, California, USA, 2007, ISBN: 978-1-4244-1328-7.

[39]    Bukovsky, I., Bila, J., Gupta, M., M.: “Stable Neural Architecture of Dynamic Neural Units with Adaptive Time Delays”, 7th International FLINS Conference on Applied Artificial Intelligence, 2006, p. 215-222, ISBN 981-256-690-2.

    click to see the full  list of my publications

Research Project Reports

[40]   Bukovský, I.: Křehlík, K.: Testy neuronového modelu kotle elektrárny Mělník I, research report (Výzkumná zpráva č. 8-ZI00069/ E06) for I. & C. Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2011, 61 pages.

[41]   Bukovský, I.: Křehlík, K.: Otestování metody extrapolace pyrometrických měření na základě neuronových sítí, research report # 7-ZI00069/E05 for I. & C. Energo, a.s., U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2011, 16 pages.

[42]   Bukovsky, I.: Návrh regulačního algoritmu kotle na základě neuronového modelu elektrárny Mělník I, research report (Výzkumná zpráva č. 8-ZI00069/ E02) for I. & C. Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2011, 6 pages.

[43]   Bukovsky, I..: Dynamické neuronové sítě pro nestacionární modely a validaci veličin energetických procesů (Dynamical Neural Networks for Nostationary Models and for Validation of Variables of Energetic Processes), Výzkumná zpráva č. 4 pro I. & C. Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2010, 40 pages.

[44]   Bukovsky, I.: Návrh metodiky extrapolace obrazců řezu spalovací komorou (Development of Extrapolation Method for Thermal Images in Combustion Chamber),  Výzkumná zpráva č. 5 pro I. & C. Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2010, 6 pages.

[45]   Bukovsky, I.; Nonconventional neural networks and validation of process data, research report for I & C Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2009, 93 pages.

[46]   Bukovsky, I., Bila. J: Program system for advanced reconciliation of process data: Program System I&C NEURECON (In Czech, Programový systém pro pokročilé validování provozních dat: Programový systém I&C NEURECON), Final report for I&C Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2008, 107 pages. (confidential)

[47]   Bukovsky, I., Bila. J: Analysis of Methods for Evaluation of Data Uncertainty for Control of Energetic Systems (In Czech, Analýza metod pro stanovování neurčitostí dat pro řízení provozu energetických zařízení), report for I&C Energo, a.s. U12110, Faculty of Mechanical Engineering, Czech Technical University in Prague, 2007, 128 pages. (confidential)

[48]   Bukovsky, I. : Development of Higher-Order Nonlinear Neural Units as a Tool for Approximation, Identification and Control of Complex Nonlinear Dynamic Systems and Study of Their Application Prospects for Nonlinear Dynamics of Cardiovascular System, Final report from scientific research under NATO Science Fellowships at the Intelligent System Research Laboratory at the University of Saskatchewan in Canada from April to October 2003 partially supported by Internal Grant of Czech Technical University (IGS #CTU0304112), 2003,32 pages.

      click to see the full  list of my publications

-          IEEE CIS Neural Networks Technical Committee (2007, 08, 09, 10, 11,12)

-          IEEE CIS NNTC Task Force on Education (chair 2009, 10, 11, 12)

-          IEEE CIS Student Activity Subcommittee (Vice Chair 2010, 11, 12)

-          Graduate Student Research Grants (2011)

Workshop Organization

-          CompSens 2011, IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology, within the IEEE Symposium Series on Computational Intelligence 2011, Paris, 2011.

Special Session Organization

-          I. Bukovsky, T. Wagner, J. Pitel,: IEEE CompSens 2013, session on Merging Fields of Computational Intelligence and Sensor Technology, within the IEEE Symposium Series on Computational Intelligence 2013, Singapore, 2013.

-          S.Y. Fu, N. Homma, I. Bukovsky, A.M.G. Solo, and M.M. Gupta: Biologically Inspired Sensing, Computing and Control Session, within the The seventh International Conference on Intelligent Systems and Knowledge Engineering (ISKE2012), Beijing, China, 2012.

 

Editorial

-          A/E IEEE Transactions on Neural Networks, 2011,12,13.

-          Cognitive and Neural Aspects in Robotics 2011, annual issue of Journal of Robotics, Hindawi Publishing Corporation (Jan 2012)

-          Cognitive and Neural Aspects in Robotics with Applications 2010, special issue on Journal of Robotics, Hindawi Publishing Corporation

Conference Activities

- Program Committee of IEEE International Joint Conference on Neural Networks, USA, 2013.

- Program Committee of 12th  IEEE International Conference on Cognitive Informatics & Cognitive Computing ICCI*CC, USA, 2013.

- Program Committee of 5th International Conference on Neural Computation Theory and Applications (NCTA 2013), part of International Joint Conference on Computational Intelligence IJCCI, Portugal 2013.

- Program Committee of 14th EANN Conference, Engineering Applications of Neural Networks, Greece, 2013.

- Program Committee of The 25th International Conference on Software Engineering and Knowledge Engineering SEKE 2013 Boston, USA, 2013.

- Program Committee of 11th  IEEE International Conference on Cognitive Informatics & Cognitive Computing ICCI*CC Japan, 2012.

- Program Committee of 4th International Conference on Neural Computation Theory and Applications (NCTA 2012), Spain, 2012.

Program Committee of 9th International Symposium on Neural Networks (ISNN 2012), China, 2012.

- Program Committee of IEEE 13th Engineering Applications of Neural Networks Conference (EANN 2012), London, UK, 2012.

- Program Committee of IEEE 2011 International Joint Conference on Neural Networks USA, 2011.

- Program Committee of ICCI 2011,10th IEEE International Conference on Cognitive Informatics , Canada, 2011.

- Program Committee of 12th Engineering Applications of Neural Networks Conference (EANN), Greece, 2011

- Program Committee of CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation, Paris, 2011.

- Program Committee of ICNC 2011, International Conference on Natural Computation, China, 2011

- Technical Program Committee of IEEE IJCNN + chairing sections IA-1, SC, Spain, 2010

- Program Committee of ICANN 10 International Conference on Artificial Neural Networks, Greece, 2010.

- Program Committee of ICNC 2010, International Conference on Neural Computation, Spain, 2010.

- Program Committee of ICCI 2010, 9th IEEE International Conference on Cognitive Informatics , China, 2010.

- Program Committee of ICNC 2010, International Conference on Natural Computation, China, 2010.

- Program Committee of ICCI 2009, 8th IEEE International Conference on Cognitive Informatics , Hong Kong, 2009.

- Program Committee of ICANN 09 International Conference on Artificial Neural Networks, Cyprus, 2009.

- Program Committee of ICNC 2009, International Conference on Neural Computation, Portugal, 2009.

- Program Committee of ICNC 2009, International Conference on Natural Computation, China, 2009.

- Program Committee of CICA 2009, IEEE Symposium on Computational Intelligence in Control and Automation, Nashville, USA, 2009.

- Chairing 2 sections (MA-6, MP-2) at 2008 IEEE International Conference on Automation and Logistics, Qingdao, China, 2008. 

- Panel discussion board member at ICCI 2008,  The 7th IEEE International Conference on Cognitive Informatics, California , USA, 2008.

- Program Committee of IJCNN 2008, the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), Hong   Kong, June 1-6, 2008

- Technical Committee of FUZZ-IEEE 2008, WCCI 2008 Hong Kong, June 1-6, 2008

- Program Committee of 2007 ISNN International Symposium on Neural Networks, Nanjing, China, June 3-7, 2007

- Co-chairing the section “Automation, Intelligence and Robotics” during the 7th International FLINS Conference on Applied Artificial Intelligence, Genova, Italy, August 29-31, 2006

Journals Reviews

- IEEE Transactions on Neural Networks, 2010,11,12
- Nuclear Engineering and Design, 2009
- Soft Computing, 2009

- IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics, 2007, 2008

- The International Journal of Cognitive Informatics and Natural Intelligence (IJCiNi), 2008, 2010

- IEEE Transactions on Control Systems Technology, 2007, 2009

- IEEE Computational Intelligence Magazine, 2006, 2008

- Automatizace, Czech Republic, 2006

Book chapter reviews

-  Advances in Cognitive Informatics, LNAI, 2009 (1x) 
-  Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications, 2009 (1x)

 

-          Project review for NSERC, Canada, 2009

-          Consulting the use of neural networks and nonlinear methods for classification of traffic data for ELTODO EG, a.s., 2008

-          www.konmep1.eu -  International visiting research arrangements for PhD students

-          Projekt U3V – Univerzita třetího věku

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